Zhenyu "James" Kong
Main Area: Manufacturing Systems Engineering
- Sensing and analytics for smart manufacturing
- Modeling, synthesis, and diagnosis for large and complex manufacturing systems
- Data mining and machine learning for manufacturing and service applications
- Ph.D., Industrial and Systems Engineering, University of Wisconsin-Madison, 2004
- M.S., Mechanical Engineering, Harbin Institute of Technology, China, 1995
- B.S., Mechanical Engineering, Harbin Institute of Technology, China, 1993
- Associate Professor, Department of Industrial and Systems Engineering, Virginia Tech, 08/2013 – present.
- Associate Professor, School of Industrial Engineering and Management, Oklahoma State University, 07/2012 – 08/2013.
- Assistant Professor, School of Industrial Engineering and Management, Oklahoma State University, 08/2006 - 06/2012.
- Senior Research Engineer, Dimensional Control Systems, Troy, Michigan, 07/2004 - 07/2006.
- Research Assistant, University of Wisconsin-Madison, 09/2000 - 07/2004.
- Research Fellow, University of Michigan-Ann Arbor, 11/1998 - 09/2000.
- ISE 3214: Facilities and Logistics
- ISE 6284: Advanced Topics in Manufacturing Systems Engineering
- ISE 5984: Sensing and Data Analytics for Complex Systems
- ISE 4984: Data Analytics in Manufacturing and Service Systems
- ISE 2214: Manufacturing Process Laboratory
- ISE 4404: Statistical Quality Control
Please click a title to view the abstract.
- Liu, J., Beyca, O., Rao, P., Kong, Z., and Bukkapatnam, S., 2016, “A Recurrent Nested Dirichlet Process (RNDP) Model for Evolutionary Clustering Analysis and its Application to Online Monitoring of Ultraprecision Manufacturing Processes,” IEEE Trans. Journal of Automation Science and Engineering (in press).
- Tootooni, M., Liu, C., Robertson, D., Rao, P., and Kong, Z., 2016, “Online Non-contact Surface Finish Measurement in Machining using Graph-based Image Analysis,” Journal of Manufacturing Systems, Vol. 41 pp. 266-276.
- Tootooni, M., Rao, P., Chou, C., and Kong, Z., 2016, “A Spectral Graph Theoretic Approach for Monitoring Multivariate Time Series Data From Complex Dynamical Processes,” IEEE Trans. Journal of Automation Science and Engineering, in press.
- Bastani, K., Kim, W., Kong, Z., Nussbaum, M., and Huang, W., 2016, “Online Classification and Sensor Selection Optimization with Applications to Human Material Handling Tasks Using Wearable Sensing Technologies,” IEEE Trans Journal of Human-Machine Systems, Vol. 46, No., 4, pp. 485-497, DOI: 10.1109/THMS.2016.2537747.
- Bastani, K., Rao, P., and Kong, Z., 2016, “An Online Sparse Estimation based Classification Approach for Real-time Monitoring in Advanced Manufacturing Processes from Heterogeneous Sensor Data,” IIE Trans, Vol. 48, No. 7, pp. 579-598. DOI: 10.1080/0740817X.2015.1122254.
- Bastani, K., Kong, Z., Huang, W., and Zhou, Y., 2016, “Compressive Sensing Based Optimal Sensor Placement and Fault Diagnosis for Multi-Station Assembly Processes,” IIE Trans, Vol. 48, No. 5, pp.462-474. DOI:10.1080/0740817X.2015.1096431.
- National Science Foundation
- Department of Transportation
- National Institute of Standards and Technology
- Commonwealth Center for Advanced Manufacturing
- Member of Institute of Industrial Engineers (IIE)
- Member of Institute for Operation Research and the Management Sciences (INFORMS)
- Member of American Society of Mechanical Engineering (ASME)
- Member of Society of Manufacturing Engineers (SME)
- Member of Institute of Electrical and Electronics Engineers (IEEE)
- Paper titled “A Graph Theoretic Approach for Quantification of Surface Morphology and its Application to Chemical Mechanical Planarization (CMP) Process” (IIE Trans., Vol. 47. No. 10, pp. 1088-1111) selected for Best Applications Paper Honorable Mention designation in the IIE Transactions Forcused Issue on Quality and Reliability Engineering Best Paper Award Competition for 2017.
- Paper titled “Compressive sensing based optimal sensor placement and fault diagnosis for multistation assembly processes” (IIE Trans., Vol. 48, No., 5, pp. 462-474) selected for Best Applications Paper Honorable Mention designation in the IIE Transactions Focused Issue on Design and Manufacturing Best Paper Award Competion for 2017.
- Paper titled “High-dimensional Process Monitoring and Change Point Detection using Embedding Distribution in Reproducing Kernel Hilbert Space (RKHS)” (IIE Trans, Vol. 46, No. 10, pp. 999-1016) selected for Best Applications Paper Honorable Mention designation in the IIE Transactions Focused Issue on Quality and Reliability Engineering Best Paper Award Competition for 2015.
- Halliburton Outstanding Faculty Award, College of Engineering, Architecture and Technology, Oklahoma State University, 2013
- Richard S and Harriet K. Fein Scholarship, the University of Wisconsin-Madison, 2004
- (540) 231-9762
123 Durham Hall
1145 Perry Street
Blacksburg, VA 24061
Sensing and Analytics for Smart Manufacturing
(Durham Hall 191)